Abstract
The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the optical flow and the flow inpainted from the surrounding areas. It provides the cue for motion saliency. The method is flexible and general by relying on motion information only. Experimental results on the DAVIS 2016 benchmark demonstrate that the method compares favourably with state-of-the-art video saliency methods.
Abstract (translated)
本文讨论了视频中运动显著性的问题,即识别出偏离上下文的运动区域。我们提出一个新的无监督模式来计算运动显著性地图。关键的组成部分是喷涂的流动阶段。候选区域由光流边界确定。这些区域中的剩余流量是由光流量和周围区域注入的流量之间的差异给出的。它为运动显著性提供了线索。该方法仅依靠运动信息,具有灵活性和通用性。Davis 2016基准测试的实验结果表明,该方法与最先进的视频显著性方法相比具有优势。
URL
https://arxiv.org/abs/1903.04842